SOTAVerified

Trajectory Planning

Trajectory planning for industrial robots consists of moving the tool center point from point A to point B while avoiding body collisions over time. Trajectory planning is sometimes referred to as motion planning and erroneously as path planning. Trajectory planning is distinct from path planning in that it is parametrized by time. Essentially trajectory planning encompasses path planning in addition to planning how to move based on velocity, time, and kinematics.

Papers

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Showing 251–260 of 324 papers

TitleStatusHype
Formation Control for Connected and Automated Vehicles on Multi-lane Roads: Relative Motion Planning and Conflict ResolutionCode0
Deep 6-DoF Tracking of Unknown Objects for Reactive Grasping—0
Efficient UAV Trajectory-Planning using Economic Reinforcement Learning—0
Real-Time Optimal Trajectory Planning for Autonomous Vehicles and Lap Time Simulation Using Machine Learning—0
Optimal Trajectory Planning and Model Predictive Control of Underactuated Marine Surface Vessels using a Flatness-Based Approach—0
An Efficient Generation Method based on Dynamic Curvature of the Reference Curve for Robust Trajectory Planning—0
Amortized Q-learning with Model-based Action Proposals for Autonomous Driving on Highways—0
Planning Brachistochrone Hip Trajectory for a Toe-Foot Bipedal Robot going Downstairs—0
Safe Trajectory Planning Using Reinforcement Learning for Self Driving—0
Trajectory Planning for Autonomous Vehicles Using Hierarchical Reinforcement LearningCode1
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All datasetsnuScenesToolBench

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ST-P3 (Lidar)Collision-3s1.27—Unverified
2UniADCollision-3s0.71—Unverified
3AD-MLPCollision-3s0.24—Unverified
4VAD-Base [jiang2023vad]Collision-3s0.24—Unverified
#ModelMetricClaimedVerifiedStatus
1GPT4-TOPGUNWin rate86.54—Unverified
2Attention BucketWin rate71.5—Unverified
3GPT4- DFSDTWin rate70.4—Unverified